{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "D:\\myGit\\github\\facenet\\src\n"
     ]
    }
   ],
   "source": [
    "import os\n",
    "os.chdir(r\"D:/myGit/github/facenet/src\")\n",
    "print(os.getcwd())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import sys \n",
    "sys.path.append(\"../contributed\")\n",
    "import face\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.image as mpimg\n",
    "from PIL import Image\n",
    "import cv2\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 计算特征向量 512d f32ndarray"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "#%prun faceRecognition=face.Recognition()\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Wall time: 282 ms\n",
      "[ 70  94 329 411]\n"
     ]
    }
   ],
   "source": [
    "face_image1=cv2.imread(\"X:/share/image/mydataset/test_img/amellanby/amellanby1.jpg\")\n",
    "%time face1=faceRecognition.add_identity(face_image1,\"amellanby\")\n",
    "#face1[0].embedding\n",
    "\n",
    "print(face1[0].bounding_box)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* 比对速度平均为6.67us \\次 这是否意味着，classifier 是快速查询，实际上一般情况下可以通过遍历完成1：N识别？"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "6.67 µs ± 231 ns per loop (mean ± std. dev. of 7 runs, 100000 loops each)\n",
      "1.0174652\n"
     ]
    }
   ],
   "source": [
    "# 距离\n",
    "import numpy as np\n",
    "#\n",
    "face_image2=cv2.imread(\"X:/share/image/mydataset/test_img/andrew/andrew10.jpg\")\n",
    "face2=faceRecognition.add_identity(face_image2,\"andrew10\")\n",
    "\n",
    "emb1=face1[0].embedding\n",
    "emb2=face2[0].embedding\n",
    "%timeit dist = np.sqrt(np.sum(np.square(np.subtract(emb1, emb2))))\n",
    "print(dist)  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# API LIB\n",
    "2个目的，一个本地函数LIB，一个网络接口共第三者调用\n",
    "分离人类识别计算，和文件管理等逻辑过程"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 初始化 读取配置"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "D:\\myGit\\github\\facenet\\src\n",
      "Model directory: X:/share/model/20180402-114759\n",
      "Metagraph file: model-20180402-114759.meta\n",
      "Checkpoint file: model-20180402-114759.ckpt-275\n",
      "INFO:tensorflow:Restoring parameters from X:/share/model/20180402-114759\\model-20180402-114759.ckpt-275\n"
     ]
    }
   ],
   "source": [
    "# 初始化\n",
    "# 载入配置文件\n",
    "import os\n",
    "os.chdir(r\"D:/myGit/github/facenet/src\")\n",
    "print(os.getcwd())\n",
    "import sys \n",
    "sys.path.append(\"../contributed\")\n",
    "import face\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.image as mpimg\n",
    "from PIL import Image\n",
    "import cv2\n",
    "faceRecognition=face.Recognition()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 特征提取接口\n",
    "* 一次数据量为6.12kbytes\n",
    "* json b'  {name(str):embedding(float64 list)}  需要转换 float64-float32\n",
    "\n",
    "读取图片-2进制流传输图片-解析图片-提取特征-输出\n",
    "* 2个方式 本地计算-网络传输 速度慢流量小 ，网络传输-服务器计算 速度快流量大，需要权衡分配那些地方放在本地那些在服务器"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "import sys\n",
    "sys.path.append(r\"C:\\Users\\user\\OneDrive\\code space\\python\\1notebook\\lib\")\n",
    "import decoration\n",
    "from kafka import KafkaProducer\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'id': 'L',\n",
       " 'embedding': array([-0.00150362, -0.0614731 , -0.01542308, -0.00643886,  0.09229134,\n",
       "         0.00879708, -0.02503851,  0.07556674, -0.06846002,  0.03993122,\n",
       "         0.00428674, -0.01728249, -0.05609911, -0.00582005, -0.01823286,\n",
       "         0.02961844, -0.00084985,  0.05152377, -0.01019109, -0.05480282,\n",
       "        -0.00018367,  0.01569144, -0.01059835, -0.02458817, -0.06018485,\n",
       "        -0.06629568,  0.00218294,  0.03329042,  0.01928552, -0.00787686,\n",
       "        -0.08434576, -0.10381334,  0.0307178 ,  0.01405045,  0.03978757,\n",
       "         0.02693323, -0.0343332 , -0.01531746, -0.03156816,  0.02357127,\n",
       "        -0.03435097,  0.12176359,  0.09398597,  0.02146244,  0.09838551,\n",
       "         0.12445129, -0.01476614, -0.02868221, -0.06635207,  0.01967485,\n",
       "         0.00874948,  0.04264139,  0.0717226 ,  0.04428186, -0.05402932,\n",
       "         0.01002627,  0.04020431,  0.03421589,  0.0059094 , -0.03501022,\n",
       "         0.02812987, -0.05137388,  0.01343469, -0.05468477, -0.05910308,\n",
       "         0.02267227,  0.00345957,  0.03225706,  0.01930469,  0.05232833,\n",
       "        -0.04043398, -0.06376576,  0.04916322, -0.04965717,  0.02964324,\n",
       "         0.01758414, -0.00254208,  0.06497184, -0.03225593,  0.02876783,\n",
       "        -0.00488168,  0.09585287, -0.0002819 ,  0.0428548 , -0.02536149,\n",
       "         0.07898068,  0.0248543 ,  0.04971014, -0.02252082,  0.03497192,\n",
       "        -0.04097691, -0.04405929,  0.02964178, -0.06993248, -0.0293349 ,\n",
       "        -0.00229433, -0.00605602,  0.08750855,  0.01109493, -0.04955739,\n",
       "         0.02175519,  0.0115204 , -0.00714504,  0.04346544, -0.02469115,\n",
       "        -0.0555421 ,  0.0402883 , -0.04100242, -0.01858751, -0.06703478,\n",
       "        -0.07632965,  0.04059967, -0.03550918, -0.09092128,  0.06225526,\n",
       "        -0.01140692,  0.01877856,  0.01353691,  0.03785875, -0.03493613,\n",
       "        -0.04676229, -0.0413902 , -0.01775126, -0.01464722,  0.0578943 ,\n",
       "         0.00135145, -0.02306723,  0.04591985,  0.02088857,  0.01828153,\n",
       "        -0.0625505 , -0.00707023,  0.02295181, -0.01097745,  0.03863392,\n",
       "        -0.03638836,  0.03509097,  0.04192273, -0.02013863,  0.04381118,\n",
       "         0.03136646,  0.01283335, -0.00946276,  0.03859644,  0.00627405,\n",
       "        -0.0387281 , -0.00558538,  0.01978849, -0.00187165,  0.05993392,\n",
       "         0.03565454, -0.07207937, -0.09973998,  0.03654721, -0.04430696,\n",
       "        -0.04842867, -0.06798108,  0.00350045,  0.01362762, -0.0542845 ,\n",
       "         0.00858831, -0.02413159, -0.08787485,  0.04882977,  0.03115057,\n",
       "         0.05327047, -0.04411985, -0.02790711,  0.02277046,  0.04598274,\n",
       "         0.01699665, -0.02895444, -0.04655507, -0.05285815,  0.02459554,\n",
       "         0.05530347,  0.0854342 ,  0.00799674, -0.04823921, -0.07786462,\n",
       "        -0.03622766, -0.00545372, -0.00319665,  0.00706894,  0.04865038,\n",
       "         0.00047535, -0.02061821, -0.06651205,  0.04800637, -0.01012065,\n",
       "         0.08208093,  0.04702618,  0.04114789,  0.00196191,  0.05881394,\n",
       "        -0.06737909, -0.03600431, -0.00233677, -0.09843207,  0.03198095,\n",
       "        -0.00599921,  0.06462102,  0.01510603, -0.09852924, -0.06193781,\n",
       "         0.09841271, -0.03999396,  0.0029862 ,  0.01725776,  0.04636862,\n",
       "         0.03679011, -0.00929184, -0.01012203, -0.05251572, -0.03626921,\n",
       "         0.00375934, -0.09054816,  0.03163711, -0.02540569,  0.06110237,\n",
       "        -0.01176079, -0.02206801, -0.01406379,  0.00922101, -0.00134087,\n",
       "        -0.0288855 ,  0.00288895, -0.0095426 , -0.02606596,  0.0428902 ,\n",
       "         0.00951312,  0.01040952,  0.00084069, -0.02084743,  0.06592885,\n",
       "        -0.06724929,  0.09369389, -0.08306959,  0.01116591,  0.08486702,\n",
       "         0.03362359, -0.00308518,  0.02316775,  0.0246601 , -0.05925291,\n",
       "        -0.03689847, -0.05936748,  0.02682525, -0.01298645,  0.03609169,\n",
       "         0.05659921,  0.0754483 , -0.04428053, -0.03547015,  0.03753688,\n",
       "         0.03848762, -0.03044004,  0.08276657,  0.02741598,  0.02131171,\n",
       "        -0.0338386 , -0.04476088,  0.0080169 ,  0.04908546, -0.06800398,\n",
       "         0.00421699,  0.0624905 , -0.01336201, -0.01329152, -0.00380631,\n",
       "        -0.05151752, -0.00255838, -0.01257857, -0.03324098,  0.02399303,\n",
       "        -0.10363972, -0.00071074,  0.04413922, -0.00820693, -0.01883999,\n",
       "        -0.00526875,  0.02849261,  0.05265874, -0.09731547,  0.00749553,\n",
       "         0.01884175,  0.08449051,  0.03946571, -0.00917335, -0.07901888,\n",
       "         0.04447628,  0.02241716, -0.00387706,  0.01353009,  0.06668959,\n",
       "        -0.0118743 ,  0.02987772,  0.08917609,  0.0295369 , -0.05274592,\n",
       "         0.06273384,  0.05429738,  0.01121501,  0.00755492,  0.04365101,\n",
       "         0.02353705,  0.07771718, -0.05397661,  0.01476647,  0.03707414,\n",
       "        -0.02695023,  0.01564219, -0.04348426,  0.0324419 ,  0.08441066,\n",
       "        -0.02169711, -0.02266923,  0.01236882,  0.07265607, -0.01238608,\n",
       "         0.01071864,  0.00709165,  0.04088021, -0.07283154, -0.05246034,\n",
       "        -0.00032165, -0.00236911, -0.0351776 ,  0.00712981,  0.00025607,\n",
       "        -0.05653728, -0.02447528, -0.01128803,  0.02167019,  0.02602391,\n",
       "         0.05049534,  0.00386152,  0.01138064, -0.07078038, -0.04266178,\n",
       "        -0.01025592,  0.11663719,  0.06448629,  0.05321003, -0.09621334,\n",
       "         0.04400744, -0.04967367,  0.03112882,  0.06909193,  0.04189115,\n",
       "         0.04191273, -0.00539326,  0.01550249,  0.0579583 , -0.03522618,\n",
       "        -0.07883463,  0.00131971,  0.05085506, -0.07703186, -0.03722671,\n",
       "        -0.0114868 ,  0.00487254,  0.00068614,  0.06126519, -0.03455237,\n",
       "         0.00181385,  0.06062543,  0.05662263,  0.03216256, -0.04504052,\n",
       "        -0.02893701,  0.02626671, -0.0394909 , -0.01727507, -0.05879057,\n",
       "         0.08700088, -0.0048662 , -0.08630983, -0.01703417,  0.0147212 ,\n",
       "         0.04940442, -0.03910718, -0.05874744,  0.01891592,  0.01924563,\n",
       "         0.05491937,  0.04739726, -0.09507626, -0.00605434, -0.0428595 ,\n",
       "        -0.00040672,  0.09571592,  0.00482078, -0.01824265,  0.0054494 ,\n",
       "         0.05580263,  0.069811  ,  0.00783406,  0.07515837,  0.05031152,\n",
       "         0.01766554, -0.02813761,  0.00206249, -0.0454858 ,  0.05436658,\n",
       "         0.0642073 , -0.03958255, -0.04960347,  0.04209241, -0.02801908,\n",
       "         0.02985543,  0.0558222 , -0.01223686,  0.02284104, -0.02256123,\n",
       "        -0.03994664, -0.0144978 , -0.0057584 , -0.00439523,  0.07629672,\n",
       "         0.00683723,  0.01197539,  0.03346825, -0.0527346 , -0.04980565,\n",
       "         0.03721754,  0.07236125, -0.03947356, -0.03326758, -0.02908099,\n",
       "        -0.01010643,  0.06862934, -0.03337865, -0.01088061, -0.00475242,\n",
       "        -0.00290742,  0.04131093,  0.03214423,  0.03093839, -0.01151702,\n",
       "        -0.05638302, -0.03141822, -0.02513374,  0.00188227,  0.02260893,\n",
       "         0.00723265, -0.01912502,  0.0258954 , -0.03345015, -0.12453222,\n",
       "         0.02119397,  0.06919764,  0.04321462, -0.01766015, -0.02280711,\n",
       "        -0.01043798,  0.03260814,  0.048705  , -0.02104996, -0.01072985,\n",
       "        -0.01303268,  0.02434172,  0.048459  ,  0.01130145,  0.03844652,\n",
       "         0.00534581, -0.02249478,  0.00730619, -0.06679803,  0.0172854 ,\n",
       "        -0.03912206,  0.01804016, -0.01446068, -0.04066491, -0.04543801,\n",
       "         0.05825704,  0.07429531,  0.00970104,  0.01984423, -0.02400338,\n",
       "        -0.01634758, -0.01347003, -0.01947786, -0.06828078,  0.03281288,\n",
       "         0.03603638, -0.06754462, -0.01659003,  0.02469539, -0.01816709,\n",
       "        -0.0223964 , -0.04333543,  0.03798084,  0.01006981,  0.01674609,\n",
       "        -0.0151786 ,  0.07514774, -0.00377792, -0.00273713,  0.05339961,\n",
       "        -0.08145328,  0.04305134, -0.01089347, -0.00846507, -0.03924002,\n",
       "         0.06980553,  0.06831924, -0.00481119, -0.03250369, -0.0561296 ,\n",
       "        -0.05431145, -0.04394893], dtype=float32)}"
      ]
     },
     "execution_count": 130,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import io\n",
    "from PIL import Image\n",
    "import numpy as np\n",
    "def generateEmbedding_iostream(imgStream,id=\"\",imageMode=None):\n",
    "    face_image1= np.array( Image.open(io.BytesIO(imgStream)))\n",
    "    #face_image1=cv2.cvtColor(face_image1, cv2.COLOR_RGB2GRAY)\n",
    "    if imageMode:\n",
    "        face_image1=cv2.cvtColor(face_image1, imageMode)\n",
    "        \n",
    "    face1=faceRecognition.add_identity(face_image1,id)\n",
    "    assert len(face1)==1,\"more then one face detect\"    \n",
    "    return {\"id\":face1[0].name,\"embedding\":face1[0].embedding}\n",
    "def generateEmbedding_filename(imgPath,id=\"\",imageMode=None):\n",
    "    face_image1=cv2.imread(imgPath) #**\n",
    "    if imageMode:\n",
    "        face_image1=cv2.cvtColor(face_image1, imageMode)\n",
    "    \n",
    "    face1=faceRecognition.add_identity(face_image1,id)\n",
    "    assert len(face1)==1,\"more then one face detect\"    \n",
    "    return {\"id\":face1[0].name,\"embedding\":face1[0].embedding}\n",
    "\n",
    "with open(r\"X:/share/image/mydataset/test_img/andrew/andrew10.jpg\",\"rb\") as im:\n",
    "    img=im.read()\n",
    "    face1=generateEmbedding_iostream(img,\"L\", cv2.COLOR_RGB2BGR)   \n",
    "\n",
    "face2=generateEmbedding_filename(r\"X:/share/image/mydataset/test_img/andrew/andrew10.jpg\",\"\",cv2.COLOR_RGB2BGR)\n",
    "    \n",
    "    \n",
    "    \n",
    "face1\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##  11特征对比\n",
    "**注意！ opencv 读取图像为BGR 通道 。 facenet 只接受BGR或RGB 三通道的图片**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 158,
   "metadata": {},
   "outputs": [],
   "source": [
    "import doctest\n",
    "\n",
    "def featureCompare(emb1,emb2):\n",
    "    \"\"\"\n",
    "    generateEmbedding_filename()\n",
    "    \"\"\"\n",
    "    assert len(emb1)==len(emb2) , \"embedding dimension not same\"\n",
    "    dist = np.sqrt(np.sum(np.square(np.subtract(emb1, emb2))))   \n",
    "    return dist\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "emb1=face1[\"embedding\"]\n",
    "emb2=face2[\"embedding\"]\n",
    "\n",
    "assert dist==0.0 # 2种打开图片的方法会得到不同特征向量!!!! 必须统一，可能是通道不一样,确实是的\n",
    "producerInfoMap={\"producer\": KafkaProducer(bootstrap_servers=\"192.168.1.201:9092\"),\"topic\":\"test\",\"key\":b'123'}\n",
    "\n",
    "@decoration.output2kafka(**producerInfoMap)\n",
    "@decoration.kvFaceEmbedding\n",
    "def faceEmbedding2kafka(img):\n",
    "    return generateEmbedding(img)\n",
    " \n",
    "\"end\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 上传图片\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-----记录日志-----send 1541645310.780682\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "'end'"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# png等格式是压缩的图片，读出2进制后会读出很大的图片吗？\n",
    "img_path=\"X:/share/image/mydataset/test_img/andrew/andrew10.jpg\"\n",
    "producerInfoMap[\"key\"]=b'upimg'\n",
    "producerInfoMap[\"partition\"]=None\n",
    "@decoration.output2kafka(**producerInfoMap)\n",
    "def readImage(img_path):\n",
    "    with open(img_path,\"rb\") as img:\n",
    "        imgbytes=img.read()\n",
    "    return imgbytes\n",
    "readImage(img_path)\n",
    "\"\"\"end\"\"\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 接受 解析图片"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "from kafka import KafkaConsumer\n",
    "sys.path.append(r\"C:\\Users\\user\\OneDrive\\code space\\python\\1notebook\\lib\")\n",
    "import sjasync\n",
    "import asyncio\n",
    "consumerInfoMap={\"bootstrap_servers\":\"192.168.1.201:9092\",\"group_id\":'group2220'}\n",
    "consumer = KafkaConsumer(\"test\",**consumerInfoMap)\n",
    "\n",
    "async def akafkaConsumer(consumer):\n",
    "    #assert tyep(consumer) is KafkaConsumer   \n",
    "    re=next(consumer)\n",
    "    #print(re)\n",
    "    return re\n",
    "async def getImageFkafka(consumer,savePath=\"./t.jpg\"):\n",
    "    \"\"\"\n",
    "    存储文件\n",
    "    文件根路径，文件夹，文件名\n",
    "    \"\"\"\n",
    "    t=await akafkaConsumer(consumer)\n",
    "    if t.key == b\"upimg\":\n",
    "        print(t.offset,t.value[:10])\n",
    "        with open(savePath,\"wb\")as f:\n",
    "            f.write(t.value)\n",
    "    else:\n",
    "        print(\"key no img\")\n",
    "    return savePath\n",
    "#getImageFkafka=await akafkaConsumer(consumer)\n",
    "loop = asyncio.get_event_loop() # 开始事件循环"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-----记录日志-----send 1541650927.815864\n",
      "-----记录日志-----send 1541650928.159612\n",
      "b'{\"id\": \"\", \"embedding\": [-1.5798957017380613e-12, -1.6083929066496807e-20, 2.1862037025198826e-19, 6.084676989706653e-12, 3.47424655348099e-14, -5.130992690962721e-17, -6.503422382221962e-21, 4.1376466348694186e-15, 2.7059425186849946e-13, -4.681731282901934e-13, 2.8699798278440456e-17, -7.057214814293183e-16, -2.702740358062752e-12, 1.0816937560363462e-14, -1.1556542786285002e-19, -7.497773185189155e-11, 9.628820657853488e-18, 1.5896743682412362e-15, -2.338522625366309e-17, 4.745043580862896e-16, 3.6920882761638753e-10, 2.2079758620841835e-16, 4.4930864392518305e-10, -2.9936782628349697e-15, 1.1918534324243222e-16, 5.4461541467170966e-14, 7.595168203799092e-14, 5.494310395853844e-19, 1.2500344447438365e-14, -1.3945233540945454e-14, -2.618695707092073e-13, -4.545882411342998e-13, 3.570931536421834e-16, 8.93725626257609e-15, 3.174258245116335e-13, -2.113697652418674e-12, -1.9465876820610956e-13, 5.568221155091567e-17, 2.3945112057362725e-12, 3.0715476995209216e-15, 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     ]
    },
    {
     "data": {
      "text/plain": [
       "'go'"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "31 b'\\xff\\xd8\\xff\\xe0\\x00\\x10JFIF'\n"
     ]
    }
   ],
   "source": [
    "#readImage(img_path)# 这个发送函数在一次接受之后死循环,不是 ，是接受函数 进图了阻塞\n",
    "#原因 这是这是 jupyter 不同cell 引起的。\n",
    "\n",
    "\n",
    "loop.create_task(getImageFkafka(consumer)) #读取\n",
    "readImage(img_path) #上传\n",
    "emb1=generateEmbedding(\"t.jpg\")\n",
    "print(emb1)\n",
    "f\"{'go'}\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#readImage(img_path)\n",
    "print(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
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Company\\x00\\x00desc\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x12sRGB IEC61966-2.1\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x12sRGB IEC61966-2.1\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00XYZ \\x00\\x00\\x00\\x00\\x00\\x00\\xf3Q\\x00\\x01\\x00\\x00\\x00\\x01\\x16\\xccXYZ \\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00XYZ \\x00\\x00\\x00\\x00\\x00\\x00o\\xa2\\x00\\x008\\xf5\\x00\\x00\\x03\\x90XYZ \\x00\\x00\\x00\\x00\\x00\\x00b\\x99\\x00\\x00\\xb7\\x85\\x00\\x00\\x18\\xdaXYZ \\x00\\x00\\x00\\x00\\x00\\x00$\\xa0\\x00\\x00\\x0f\\x84\\x00\\x00\\xb6\\xcfdesc\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x16IEC http://www.iec.ch\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x16IEC 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     ]
    }
   ],
   "source": [
    "# 数据转换\n",
    "key=bytes(face1[0].name,\"utf-8\")\n",
    "assert type(key) is bytes\n",
    "emb=bytes(face1[0].embedding)\n",
    "assert type(emb) is bytes\n",
    "#print(list(face1[0].embedding))\n",
    "bytes( str([1.2,3]) ,\"utf-8\")\n",
    "#print(emb)\n",
    "import numpy as np\n",
    "import json\n",
    "\n",
    "\n",
    "\n",
    "# @kvFaceEmbedding\n",
    "# def t():\n",
    "#     return {\"id\":face1[0].name,\"embedding\":face1[0].embedding}\n",
    "# t()\n",
    "\n",
    "#face1[0].embedding"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<module 'decoration' from 'C:\\\\Users\\\\user\\\\OneDrive\\\\code space\\\\python\\\\1notebook\\\\lib\\\\decoration.py'>"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import importlib \n",
    "importlib.reload(decoration)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "#assert b\"123\"==bytes(\"123\",\"utf-8\")\n",
    "\n",
    "with open(\"X:/share/image/mydataset/test_img/andrew/andrew10.jpg\",\"rb\") as f:\n",
    "    img=f.read()\n",
    "with open(\"tt.png\",\"wb\")as f:\n",
    "    f.write(img)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "True\n",
      "Help on function dumps in module json:\n",
      "\n",
      "dumps(obj, *, skipkeys=False, ensure_ascii=True, check_circular=True, allow_nan=True, cls=None, indent=None, separators=None, default=None, sort_keys=False, **kw)\n",
      "    Serialize ``obj`` to a JSON formatted ``str``.\n",
      "    \n",
      "    If ``skipkeys`` is true then ``dict`` keys that are not basic types\n",
      "    (``str``, ``int``, ``float``, ``bool``, ``None``) will be skipped\n",
      "    instead of raising a ``TypeError``.\n",
      "    \n",
      "    If ``ensure_ascii`` is false, then the return value can contain non-ASCII\n",
      "    characters if they appear in strings contained in ``obj``. Otherwise, all\n",
      "    such characters are escaped in JSON strings.\n",
      "    \n",
      "    If ``check_circular`` is false, then the circular reference check\n",
      "    for container types will be skipped and a circular reference will\n",
      "    result in an ``OverflowError`` (or worse).\n",
      "    \n",
      "    If ``allow_nan`` is false, then it will be a ``ValueError`` to\n",
      "    serialize out of range ``float`` values (``nan``, ``inf``, ``-inf``) in\n",
      "    strict compliance of the JSON specification, instead of using the\n",
      "    JavaScript equivalents (``NaN``, ``Infinity``, ``-Infinity``).\n",
      "    \n",
      "    If ``indent`` is a non-negative integer, then JSON array elements and\n",
      "    object members will be pretty-printed with that indent level. An indent\n",
      "    level of 0 will only insert newlines. ``None`` is the most compact\n",
      "    representation.\n",
      "    \n",
      "    If specified, ``separators`` should be an ``(item_separator, key_separator)``\n",
      "    tuple.  The default is ``(', ', ': ')`` if *indent* is ``None`` and\n",
      "    ``(',', ': ')`` otherwise.  To get the most compact JSON representation,\n",
      "    you should specify ``(',', ':')`` to eliminate whitespace.\n",
      "    \n",
      "    ``default(obj)`` is a function that should return a serializable version\n",
      "    of obj or raise TypeError. The default simply raises TypeError.\n",
      "    \n",
      "    If *sort_keys* is true (default: ``False``), then the output of\n",
      "    dictionaries will be sorted by key.\n",
      "    \n",
      "    To use a custom ``JSONEncoder`` subclass (e.g. one that overrides the\n",
      "    ``.default()`` method to serialize additional types), specify it with\n",
      "    the ``cls`` kwarg; otherwise ``JSONEncoder`` is used.\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "'{\"23\": 3}'"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "if object:\n",
    "    print(True)\n",
    "import json\n",
    "help(json.dumps)\n",
    "json.dumps({\"23\":3},*())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# API 他方他方调用"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1：1 特征对比"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "dist = np.sqrt(np.sum(np.square(np.subtract(emb1, emb2))))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1:1 图片对比"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "主题对应接口，key作为识别session session 是一个配给客户端的id？这样来区别那些是他的数据？\n",
    "请求接口地址- 返回session "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "接受图片流 （各种格式编码）- 同意格式编码处理- 运算 -输出各种"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 流处理优化\n",
    "对于kafka 流的使用还有待研究\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 优化单次运算时间\n",
    "不要重复实例化 Encode！Encode 中的所有方法在实例化时都耗费大量时间。\n",
    "所以首先要把实例对象载入内存"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model directory: X:/share/model/20180402-114759\n",
      "Metagraph file: model-20180402-114759.meta\n",
      "Checkpoint file: model-20180402-114759.ckpt-275\n",
      "INFO:tensorflow:Restoring parameters from X:/share/model/20180402-114759\\model-20180402-114759.ckpt-275\n",
      " "
     ]
    }
   ],
   "source": [
    "import tensorflow as tf\n",
    "import facenet\n",
    "from face import facenet_model_checkpoint\n",
    "sess = tf.Session()\n",
    "with sess.as_default():\n",
    "    %prun facenet.load_model(facenet_model_checkpoint)\n",
    "\"\"\"需要明白这2个函数 并且少调用\n",
    " saver = tf.train.import_meta_graph(os.path.join(model_exp, meta_file), input_map=input_map)\n",
    "        saver.restore(tf.get_default_session(), os.path.join(model_exp, ckpt_file))\n",
    "\"\"\"\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 读取图片"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "4.43 ms ± 413 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)\n"
     ]
    }
   ],
   "source": [
    "imgPath=\"X:/share/image/mydataset/test_img/andrew/andrew10.jpg\"\n",
    "with open(\"X:/share/image/mydataset/test_img/andrew/andrew10.jpg\",\"rb\") as f:\n",
    "    imgStream=f.read()\n",
    "\n",
    "%timeit face_image1=np.array(Image.open(io.BytesIO(imgStream)))\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "8.84 ms ± 512 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)\n"
     ]
    }
   ],
   "source": [
    "%%timeit \n",
    "with open(\"t.jpg\",\"wb\") as f:\n",
    "    f.write(imgStream)\n",
    "    face_image2=cv2.imread(\"t.jpg\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 测试"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 对比向量距离\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 178,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.0\n",
      "0.57783395\n",
      "0.59528667\n"
     ]
    }
   ],
   "source": [
    "#import unittest\n",
    "#!python -m unittest C:/Users/user/OneDrive/myGit/qidian/qdface/UintTest.py\n",
    "import cv2\n",
    "from PIL import Image\n",
    "class TestFeatureCompare():\n",
    "    def __init__(self,img_path=None):\n",
    "        self.img_path=\"X:/share/image/mydataset/test_img/andrew/andrew10.jpg\"\n",
    "    def Donw(self):\n",
    "        pass\n",
    "    def normal(self):\n",
    "        face_BGR=generateEmbedding_filename(self.img_path,\"\")\n",
    "        dist=featureCompare(face_BGR[\"embedding\"],face_BGR[\"embedding\"])\n",
    "        return dist\n",
    "    def test_RGB__BGR_compare(self):\n",
    "        face_RGB=generateEmbedding_filename(self.img_path,\"\",cv2.COLOR_BGR2RGB)\n",
    "        face_BGR=generateEmbedding_filename(self.img_path,\"\")\n",
    "        dist=featureCompare(face_RGB[\"embedding\"],face_BGR[\"embedding\"])\n",
    "        return dist\n",
    "    def test_BGR_GARY_compare(self):\n",
    "        im=Image.open(self.img_path).convert(\"L\")\n",
    "        im.save(\"t.jpg\")\n",
    "        face_BGR=generateEmbedding_filename(self.img_path,\"\")\n",
    "        face_GRAY=generateEmbedding_filename(\"t.jpg\",\"\")\n",
    "        dist=featureCompare(face_GRAY[\"embedding\"],face_BGR[\"embedding\"])\n",
    "        return dist\n",
    "if __name__ == '__main__':\n",
    "    t=TestFeatureCompare()\n",
    "\n",
    "    print (t.normal())\n",
    "    print( t.test_RGB__BGR_compare())\n",
    "    print( t.test_BGR_GARY_compare())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 传输编码"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 261,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      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      "Ç\u0003cnumpy.core.multiarray\n",
      "_reconstruct\n",
      "q\u0000cnumpy\n",
      "ndarray\n",
      "q\u0001K\u0000àq\u0002C\u0001bq\u0003çq\u0004Rq\u0005(K\u0001M\u0000\u0002àq\u0006cnumpy\n",
      "dtype\n",
      "q\u0007X\u0002\u0000\u0000\u0000f4q\bK\u0000K\u0001çq\tRq\n",
      "(K\u0003X\u0001\u0000\u0000\u0000<q\u000b",
      "NNNJ    J    K\u0000tq\f",
      "bëB\u0000\b\u0000\u0000\f",
      "\u0015┼║7╦{╜\u0018▒|╝\u0011²╥╗>\u0003╜=ƒ!\u0010<Å\u001d",
      "═╝╝┬Ü=┼4î╜ΩÄ#=┬wî;\u0002öì╝.╚e╜'╢╛╗\u0014]ò╝aó≥<à╚^║ù\n",
      "S=ï°&╝φx`╜\u0013ù@╣WïÇ<╡ñ-╝!m╔╝däv╜\u0007╞ç╜│\u000f\u000f;è[\b=½ⁿ¥<≡\r",
      "\u0001╝x╜¼╜\u0016£╘╜µú√<┌3f<J°\"=\u0014ú▄<≈á\f",
      "╜\u0019÷z╝₧M\u0001╜ç\u0018┴<Ü│\f",
      "╜0_∙=╕{└=■╤»<X~╔=Rα■=▒φq╝≤÷Ω╝ÿπç╜)-í< Y\u000f<╝¿.=LπÆ=σ`5=╪M]╜:E$<E¡$=⌡%\f",
      "=úú┴;ßf\u000f╜£pµ<lmR╜0\u001d",
      "\\<\"²_╜\u0012\u0016r╜4╗╣<Θ╣b;·\u001f\u0004=▌$₧<<VV=\u001b₧%╜ƒùé╜`_I=ReK╜`╓≥<ƒ\f",
      "É<\u0019Ö&╗⌡\u000fà=╦\u001e",
      "\u0004╜ü¬δ<è÷ƒ╗âN─=°╦ô╣àê/=Φ┬╧╝á└í=A¢╦<▌£K=ò}╕╝╕>\u000f=h╫'╜äw4╜S╙≥<┬8Å╜╝O≡╝ì\\\u0016╗₧q╞╗»7│=Ç╟5<▒ⁿJ╜∩7▓<\u0013└<<ε Ω╗╥\b2=\u0019E╩╝\u001e",
      "Çc╜Y\u0005%=&≥'╜╫Dÿ╝êIë╜╕R£╜╫K&=\u0012r\u0011╜∩4║╜` ~=\u0019Σ:╝╒Ö<ε╔]<╟\u0011\u001b=/\u0019\u000f╜╨ë?╜─ê)╜\u0016kæ╝Θ·o╝ö\"m=4#▒:≈╝╝t\u0016<=é\u001e",
      "½<%├ò<z\u001aÇ╜d¡τ╗p\u0005╝<╞┌3╝Ö>\u001e",
      "=≈\u000b",
      "\u0015╜ì╗\u000f=+╖+=┼∙ñ╝Ys3=\u001d",
      "z\u0000=÷BR<¡\t\u001b╝M\u0017\u001e",
      "=Éû═;Zí\u001e",
      "╜Ü\u0005╖╗x\u001bó<;R⌡║D}u=\n",
      "\u0012=Y₧ô╜zD╠╜å▓\u0015=7{5╜$]F╜¬9ï╜╛ge;cF_<lY^╜ⁿ╡\f",
      "<£»┼╝╗≈│╜║\u0001H=y/ <$2Z=\u0005╖4╜s¥Σ╝\u001c",
      "ë║<fX<=É<ï<▌1φ╝ê░>╜╔üX╜ò|╔<Σàb= °«=╜\u0004\u0003<{ûE╜}wƒ╜uc\u0014╜\u001c",
      "╡▓╗▌~Q╗¢óτ;₧EG=┤8∙9äτ¿╝x7ê╜SóD=\u0017╤%╝\f",
      "\u001a¿=ç₧@=▒è(=dô\u0000;πµp=\r",
      "■ë╜Ay\u0013╜Ç$\u0019╗┴û╔╜v■\u0002=■ö─╗\u0006Xä=Gw<│╔╔╜Ç▓}╜Üî╔=┤╨#╜\u001e",
      "┤C;#`ì<\u0006φ==:▒\u0016=╥<\u0018╝π╓%╝║\u001aW╜\u0005Å\u0014╜8_v;Pq╣╜Θò\u0001=û\u001f╨╝{Fz=P░@╝·╟┤╝╨kf╝╕\u0013\u0017<&└»║Ií∞╝ûT=;îX\u001c",
      "╝Iê╒╝ó¡/=Φ▄\u001b<│î*<¥a\\:9╚¬╝┤\u0005ç= ╣ë╜òΓ┐=c ¬╜8±6<├╬¡=Σ╕\t=╣0J╗K╩╜<²\u0003╩<.│r╜┘\"\u0017╜Q+s╜ó└█<\u001c",
      "┼T╝α╘\u0013=Æ╘g=úäÜ=Ç_5╜&I\u0011╜E└\u0019=1Ñ\u001d",
      "=c]∙╝àü⌐=yùα<µò«<YÜ\n",
      "╜.W7╜VY\u0003<╫\r",
      "I=¼Eï╜╡.è;\t÷=V∞Z╝½─Y╝Gsy╗\n",
      "\u0004S╜é¬'╗Z\u0016N╝▓'\b╜\bì─<\u0010A╘╜8Q:║S╦4=^v\u0006╝QVÜ╝|Ñ¼╗SiΘ<░░W=UM╟╜\u000f¥⌡;\u0005ZÜ<\\\t¡=╠ª!=╨K\u0016╝ª╘í╜├,6=1ñ╖<?\u0016~╗T¡]<ìöê=oîB╝\u001f┬⌠<⌠í╢=_≈±<\u001a\f",
      "X╜ÜzÇ=∩f^=$┐7<;Å≈;f╦2=├╨└</*ƒ=ô\u0016]╜\u000f∩q<\u000f█\u0017=╝╞▄╝\u000b",
      "$Ç<Ä\u001c",
      "2╜╠ß\u0004=▀¼=#╛▒╝╘┤╣╝ùªJ<┤╠ö=\u0001∩J╝=¥/<\u0018aΦ;\u0003r'=┤(ò╜ªαV╜@ú¿╣\u0007C\u001b╗c\u0016\u0010╜7íΘ;òAå9úôg╜`Ç╚╝s±8╝¼à▒<\u001a0╒<4╘N=ó\u0011};ßu:<N⌡É╜\u001f╛.╜v\b(╝{▀ε=d\u0011ä=─≥Y=Ç\u000b",
      "┼╜$A4=ƒvK╜▄\u0001 <\u0012Çì=\u000fû+=»¼+=±╣░╗%■}<«em=SI\u0010╜\r",
      "tí╜'·¼:eMP=ß┬¥╜\n",
      "{\u0018╜%3<╝╒⌐ƒ;g▐3:6±z=╔å\r",
      "╜æ╛φ:_Rx=\"φg=Σ╝\u0003=h|8╜M\r",
      "φ╝G-╫<7┴!╜uäì╝a╬p╜ä-▓=átƒ╗5├░╜>ïï╝21q<I\\J=█. ╜'íp╜Å⌡Ü<\u0002⌐¥<#≤`=í#B=X╖┬╜}c╞╗qì/╜¥=╒╣╡\u0006─=₧≈¥;₧qò╝╪É▓;Læd=\u0012∙Ä=lZ\u0000<ó∞Ö=s\u0013N=P╖É<█Çµ╝╩*\u0007;QO:╜~»^=\u001e",
      "â=O!\"╜\u0003-K╜\u0017i,=Fêσ╝_ô⌠<╤Ñd=\u001f}H╝ \u001d",
      "╗<U╥╕╝\u0017ƒ#╜-êm╝÷░╝╗σ\u0005É╗tA£=╒\n",
      "α;q4D<\u0002\u0016\t==\u0000X╜\u0002\u0001L╜jq\u0018=#2ö=\u0007»!╜òC\b╜C;ε╝lò%╝èìî=\f",
      "╕\b╜ÖD2╝/║¢╗^è>╗º5)=¬⌐\u0003=ér²<▀▒<╝Γ±f╜c░\u0000╜Fσ═╝j╢÷:]6╣<╒ ∞;\u0012¼£╝û\"╘<\u0006\u0003\t╜└\n",
      " ╜·₧¡<~╖ì=╧\u00011=\u0007¼É╝°╒║╝\u0011\u0004+╝\u001e",
      "É\u0005=σ~G=⌡p¼╝=╠/╝\tçU╝Kh╟<≥|F=╢)9<\u0018z\u001d",
      "=±+»;√F╕╝├h∩;h═ê╜\u001c",
      "Üì<s> ╜≈╚ô<Ç∞l╝AÉ&╜5\u001d",
      ":╜∩₧n=$(ÿ=\u0019±\u001e",
      "<^Éó<╗ó─╝\\δà╝d▒\\╝\bÉƒ╝╦╓ï╜╠f\u0006=ßÜ\u0013=╒Tè╜╤τç╝²M╩<%╙ö╝ªx╖╝}Ç1╜╦æ\u001b=╙√$<\u001a/ë<¼»x╝\u000fτÖ=≡ûw╗ia3╗î╣Z=·╨ª╜ÜV0=ïz2╝\u0017▒\n",
      "╝%║ ╜3÷Ä=⌡Ωï=)º¥╗û\"\u0005╜'Φe╜░u^╜╠\u00034╜q\r",
      "tq\u000eb.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "'[2, 3, 56]'"
      ]
     },
     "execution_count": 261,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import json\n",
    "\n",
    "class MyEncoder(json.JSONEncoder):\n",
    "    def default(self, obj):\n",
    "        if isinstance(obj, np.integer):\n",
    "            return int(obj)\n",
    "        elif isinstance(obj, np.floating):\n",
    "            return float(obj)\n",
    "        elif isinstance(obj, np.ndarray):\n",
    "            return obj.tolist()\n",
    "        else:\n",
    "            return super(MyEncoder, self).default(obj)\n",
    "face_BGR=generateEmbedding_filename(img_path,\"\")\n",
    "# \n",
    "js=json.dumps(face_BGR[\"embedding\"],cls=MyEncoder)\n",
    "js=json.loads(js)\n",
    "np.array(js)\n",
    "\n",
    "import pickle \n",
    "import base64\n",
    "pk=pickle.dumps(face_BGR[\"embedding\"])\n",
    "b64=base64.b64encode(pk)\n",
    "#b64=base64.b64decode(b64)\n",
    "pkd=pk.decode(\"CP437\")\n",
    "\n",
    "js=json.dumps(pkd)\n",
    "# #js=json.dumps(str(b64))\n",
    "print(js)\n",
    "js=json.loads(js)\n",
    "print(js)\n",
    "pickle.loads(js.encode(\"cp437\"))\n",
    "# np 2json\n",
    "a=np.array([2,3,56],dtype=np.int32)\n",
    "json.dumps(a,cls=MyEncoder)\n",
    "\n",
    "#pickle.loads(base64.b64decode(js))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 258,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([12, 34,  5,  6], dtype=int64)"
      ]
     },
     "execution_count": 258,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a=np.array([12,34,5,6],dtype=np.int32)\n",
    "np.int64(a)\n",
    "f=np.array([0.12345,0.874641,0.454412164],dtype=np.float32)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true
   },
   "source": [
    "# log"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {
    "hidden": true
   },
   "source": [
    "# Recognition 执行过于缓慢，可能是由于初始加载的内容内有持久存储在内存中。\n",
    "# 大部分时间 在tensorflow中，可能是启动一次图计算时间长，若是这样他需要在一个持续的管道中取数据不停计算这样才能发挥。\n",
    "运行函数时间\n",
    "\n",
    "\n",
    "         16367287 function calls (16354032 primitive calls) in 76.557 seconds\n",
    "\n",
    "   Ordered by: internal time\n",
    "\n",
    "   ncalls  tottime  percall  cumtime  percall filename:lineno(function)\n",
    "       51   22.236    0.436   22.236    0.436 {built-in method tensorflow.python._pywrap_tensorflow_internal.TF_SessionRun_wrapper}\n",
    "       51   15.885    0.311   15.885    0.311 {built-in method tensorflow.python._pywrap_tensorflow_internal.ExtendSession}\n",
    "   151757   10.547    0.000   11.402    0.000 pywrap_tensorflow_internal.py:857(__init__)\n",
    "        2    6.160    3.080    6.160    3.080 {built-in method tensorflow.python._pywrap_tensorflow_internal.TF_DeleteSession}\n",
    "        1    4.211    4.211    4.211    4.211 {built-in method tensorflow.python._pywrap_tensorflow_internal.ReadFromStream}\n",
    "        1    3.401    3.401    3.401    3.401 {built-in method tensorflow.python._pywrap_tensorflow_internal.TF_GraphImportGraphDefWithResults}\n",
    "    40369    1.444    0.000    1.693    0.000 ops.py:2955(_extract_stack)\n",
    "        1    1.208    1.208   68.092   68.092 face.py:99(__init__)\n",
    "   357596    1.057    0.000    1.057    0.000 ops.py:3720(_get_operation_by_name_unsafe)\n",
    "     3794    0.529    0.000    0.529    0.000 {method 'SerializeToString' of 'google.protobuf.pyext._message.CMessage' objects}\n",
    "        1    0.388    0.388    0.486    0.486 importer.py:212(_RemoveDefaultAttrs)\n",
    "   584460    0.354    0.000    0.354    0.000 {built-in method tensorflow.python._pywrap_tensorflow_internal.TF_OperationName}\n",
    "        4    0.337    0.084    0.341    0.085 {built-in method _pickle.load}\n",
    "    40369    0.335    0.000    3.175    0.000 ops.py:1596(__init__)\n",
    "   556421    0.330    0.000    0.463    0.000 pywrap_tensorflow_internal.py:51(_swig_setattr_nondynamic)\n",
    "        1    0.314    0.314    0.314    0.314 {built-in method tensorflow.python._pywrap_tensorflow_internal.TF_DeleteGraph}\n",
    "    30369    0.294    0.000    0.294    0.000 {method 'ParseFromString' of 'google.protobuf.pyext._message.CMessage' objects}\n",
    "    41613    0.234    0.000    0.755    0.000 ops.py:2311(get_attr)\n",
    "        4    0.221    0.055    0.221    0.055 {built-in method io.open}\n",
    "   151757    0.180    0.000   12.032    0.000 c_api_util.py:125(tf_output)\n",
    "   334845    0.170    0.000    1.450    0.000 ops.py:3743(_get_operation_by_tf_operation)\n",
    "   151758    0.166    0.000    0.188    0.000 pywrap_tensorflow_internal.py:74(_swig_getattr)\n",
    "   556421    0.160    0.000    0.623    0.000 pywrap_tensorflow_internal.py:70(_swig_setattr)\n",
    "   277376    0.158    0.000    0.341    0.000 c_api_util.py:141(tf_operations)\n",
    "    40369    0.151    0.000    0.254    0.000 ops.py:3004(_add_op)\n",
    "    39625    0.150    0.000    0.164    0.000 importer.py:390(_SetDefaultAttrValues)\n",
    "    40369    0.147    0.000    0.540    0.000 ops.py:3438(_create_op_helper)\n",
    "  1833122    0.141    0.000    0.141    0.000 {method 'append' of 'list' objects}\n",
    "    50051    0.141    0.000    0.141    0.000 {built-in method tensorflow.python._pywrap_tensorflow_internal.TF_GraphGetTensorShapeHelper}\n",
    "   512741    0.139    0.000    0.714    0.000 pywrap_tensorflow_internal.py:844(<lambda>)\n",
    "    39625    0.139    0.000   12.370    0.000 ops.py:2537(_set_shape_and_handle_data_for_outputs_c_api)\n",
    "    50051    0.138    0.000   11.661    0.000 ops.py:414(_c_api_shape)\n",
    "    39626    0.137    0.000    1.796    0.000 c_api_util.py:159(new_tf_operations)\n",
    "   794709    0.133    0.000    0.135    0.000 {built-in method builtins.isinstance}\n",
    "    39625    0.132    0.000    3.658    0.000 ops.py:3407(_create_op_from_tf_operation)\n",
    "   277376    0.128    0.000    0.128    0.000 {built-in method tensorflow.python._pywrap_tensorflow_internal.TF_GraphNextOperation}\n",
    "   100962    0.119    0.000   11.613    0.000 ops.py:598(_as_tf_output)\n",
    "        1    0.118    0.118   18.679   18.679 ops.py:3521(_add_new_tf_operations)\n",
    "    39625    0.112    0.000    0.895    0.000 importer.py:344(_GetColocationNames)\n",
    "   500512    0.110    0.000    0.110    0.000 ops.py:3030(_c_graph)\n",
    "  1783616    0.106    0.000    0.106    0.000 ops.py:2991(_extract_frame_info)\n",
    "    41613    0.102    0.000    0.102    0.000 {built-in method tensorflow.python._pywrap_tensorflow_internal.TF_OperationGetAttrValueProto}\n",
    "    40369    0.100    0.000    0.704    0.000 ops.py:1748(<listcomp>)\n",
    "        1    0.099    0.099   19.950   19.950 importer.py:297(_ProcessNewOps)\n",
    "   188442    0.098    0.000    0.098    0.000 importer.py:205(_FindAttrInOpDef)\n",
    "   249615    0.085    0.000    0.202    0.000 ops.py:1850(name)\n",
    "    50795    0.083    0.000    0.153    0.000 ops.py:276(__init__)\n",
    "    52364    0.081    0.000    0.270    0.000 tensor_shape.py:503(__init__)\n",
    "    57470    0.081    0.000    0.226    0.000 ops.py:3768(_get_tensor_by_tf_output)\n",
    "    40369    0.080    0.000    0.207    0.000 ops.py:1762(_control_flow_post_processing)\n",
    "    82446    0.073    0.000    0.487    0.000 ops.py:2107(inputs)\n",
    "   151757    0.070    0.000    0.070    0.000 {built-in method tensorflow.python._pywrap_tensorflow_internal.new_TF_Output}\n",
    "   151757    0.069    0.000    0.257    0.000 pywrap_tensorflow_internal.py:846(<lambda>)\n",
    "    27473    0.064    0.000    0.112    0.000 ops.py:3592(_as_graph_element_locked)\n",
    "    40369    0.064    0.000    0.125    0.000 {built-in method tensorflow.python._pywrap_tensorflow_internal.GetOperationInputs}\n",
    "   464862    0.060    0.000    0.060    0.000 {method 'get' of 'dict' objects}\n",
    "    71073    0.058    0.000    0.074    0.000 tensor_shape.py:30(__init__)\n",
    "   151757    0.057    0.000    0.057    0.000 {built-in method tensorflow.python._pywrap_tensorflow_internal.TF_Output_oper_set}\n",
    "    66075    0.055    0.000    0.069    0.000 contextlib.py:59(__init__)\n",
    "    39625    0.052    0.000    0.139    0.000 ops.py:1923(_set_device)\n",
    "66075/63462    0.051    0.000    0.135    0.000 contextlib.py:85(__exit__)\n",
    "     2490    0.047    0.000    0.177    0.000 control_flow_ops.py:1515(_init_values_from_proto)\n",
    "      536    0.046    0.000    0.387    0.001 op_def_library.py:339(_apply_op_helper)\n",
    "    40369    0.045    0.000    0.279    0.000 ops.py:2115(<listcomp>)\n",
    "   151757    0.045    0.000    0.045    0.000 {built-in method tensorflow.python._pywrap_tensorflow_internal.TF_Output_index_set}\n",
    "    43127    0.045    0.000    0.108    0.000 {built-in method tensorflow.python._pywrap_tensorflow_internal.TF_NewBuffer}\n",
    "        1    0.044    0.044   25.122   25.122 meta_graph.py:650(import_scoped_meta_graph)\n",
    "    71474    0.043    0.000    0.125    0.000 tensor_shape.py:463(as_dimension)\n",
    "    95449    0.041    0.000    0.064    0.000 compat.py:70(as_text)\n",
    "    40369    0.041    0.000    0.046    0.000 ops.py:3784(_next_id)\n",
    "        1    0.040    0.040    0.040    0.040 {built-in method tensorflow.python._pywrap_tensorflow_internal.CreateBufferedInputStream}\n",
    "   143464    0.039    0.000    0.039    0.000 {built-in method builtins.getattr}\n",
    "        1    0.039    0.039    0.203    0.203 importer.py:153(_ProcessGraphDefParam)\n",
    "    86256    0.038    0.000    0.182    0.000 c_api_util.py:92(tf_buffer)\n",
    "    57470    0.038    0.000    0.071    0.000 control_flow_util.py:166(CheckInputFromValidContext)\n",
    "    39625    0.037    0.000    0.037    0.000 {built-in method tensorflow.python._pywrap_tensorflow_internal.SetRequestedDevice}\n",
    "115378/110152    0.037    0.000    0.262    0.000 {built-in method builtins.next}\n",
    "    40369    0.036    0.000    0.189    0.000 ops.py:1755(<listcomp>)\n",
    "    83520    0.035    0.000    0.035    0.000 {built-in method tensorflow.python._pywrap_tensorflow_internal.TF_OperationOpType}\n",
    "        1    0.035    0.035    5.489    5.489 ops.py:3539(<listcomp>)\n",
    "    50051    0.035    0.000    0.035    0.000 {built-in method tensorflow.python._pywrap_tensorflow_internal.ResourceHandleShapeAndType}\n",
    "     8684    0.032    0.000    0.032    0.000 _collections_abc.py:742(__iter__)\n",
    "    66075    0.031    0.000    0.101    0.000 contextlib.py:157(helper)\n",
    "    83520    0.031    0.000    0.066    0.000 ops.py:2218(type)\n",
    "    62526    0.030    0.000    0.061    0.000 ops.py:1863(device)\n",
    "   262462    0.030    0.000    0.030    0.000 ops.py:315(op)\n",
    "    62526    0.030    0.000    0.030    0.000 {built-in method tensorflow.python._pywrap_tensorflow_internal.TF_OperationDevice}\n",
    "    58329    0.029    0.000    0.039    0.000 dtypes.py:652(as_dtype)\n",
    "    32140    0.028    0.000    0.074    0.000 ops.py:330(name)\n",
    "   113524    0.027    0.000    0.027    0.000 ops.py:2077(outputs)\n",
    "        1    0.026    0.026   66.883   66.883 facenet.py:364(load_model)\n",
    "    40369    0.025    0.000    0.025    0.000 {built-in method tensorflow.python._pywrap_tensorflow_internal.TF_OperationNumOutputs}\n",
    "    50795    0.025    0.000    0.025    0.000 {built-in method tensorflow.python._pywrap_tensorflow_internal.TF_OperationOutputType}\n",
    "    48906    0.024    0.000    0.147    0.000 tensor_shape.py:538(<listcomp>)\n",
    "      744    0.024    0.000    0.024    0.000 {built-in method tensorflow.python._pywrap_tensorflow_internal.TF_FinishOperation}\n",
    "    43678    0.023    0.000    0.023    0.000 {built-in method tensorflow.python._pywrap_tensorflow_internal.TF_DeleteBuffer}\n",
    "   209227    0.022    0.000    0.022    0.000 pywrap_tensorflow_internal.py:864(<lambda>)\n",
    "    40369    0.022    0.000    0.031    0.000 ops.py:4488(_control_dependencies_for_inputs)\n",
    "    27473    0.022    0.000    0.134    0.000 ops.py:3557(as_graph_element)\n",
    "    40369    0.021    0.000    0.031    0.000 ops.py:4522(_record_op_seen_by_control_dependencies)\n",
    "    57470    0.021    0.000    0.026    0.000 control_flow_util.py:88(GetOutputContext)\n",
    "   126850    0.019    0.000    0.019    0.000 ops.py:2995(_check_not_finalized)\n",
    "66075/63462    0.019    0.000    0.221    0.000 contextlib.py:79(__enter__)\n",
    "   100343    0.019    0.000    0.019    0.000 {method 'HasField' of 'google.protobuf.pyext._message.CMessage' objects}\n",
    "    50796    0.018    0.000    0.031    0.000 ops.py:176(uid)\n",
    "    81138    0.018    0.000    0.018    0.000 ops.py:4907(_control_dependencies_stack)\n",
    "    43678    0.017    0.000    0.064    0.000 pywrap_tensorflow_internal.py:700(<lambda>)\n",
    "    42175    0.016    0.000    0.023    0.000 ops.py:2090(__iter__)\n",
    "   121685    0.016    0.000    0.016    0.000 ops.py:1858(_id)\n",
    "    20560    0.015    0.000    0.029    0.000 ops.py:5225(get_default)\n",
    "     2490    0.015    0.000    0.237    0.000 control_flow_ops.py:1716(_init_from_proto)\n",
    "   100547    0.015    0.000    0.015    0.000 ops.py:2227(graph)\n",
    "    39625    0.015    0.000    0.022    0.000 ops.py:1486(_device_string)\n",
    "    40420    0.015    0.000    0.015    0.000 {built-in method builtins.max}\n",
    "      551    0.015    0.000    0.016    0.000 {built-in method tensorflow.python._pywrap_tensorflow_internal.TF_NewBufferFromString}\n",
    "    92004    0.014    0.000    0.014    0.000 {built-in method builtins.iter}\n",
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